Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →majiayu000/build-script-execution-skill
Create a skill that orchestrates the write-execute-analyze loop to autonomously process data. Learn to implement error recovery, iterate toward robust solutions, and test your skill across diverse input scenarios. This is where specification-driven development meets real problem-solving.
Passed 14 of 20 test cases with the skill, and 10 of 20 without it.
and 4 more in the full report
5 fixed10 passed either way6 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
When execute_code_safely writes the generated code string to disk before calling python in a subprocess, what default script file path is used for temporary execution?
| Checked | Without skill | With skill |
|---|---|---|
| The script execution function writes code to _temp_analysis.py. | Missed | Passed |
The instructions your model reads when the skill loads.
You've learned the pattern (Lesson 5): write code from specification → execute it → analyze errors → iterate. Now you're going to build a skill that orchestrates this loop autonomously.
But here's what makes this different from following a tutorial: You'll specify what problem you're solving FIRST, then let AI help you build the skill while you validate each decision. You're not just learning a pattern—you're learning to think about error recovery, convergence criteria, and edge cases the way production systems demand.
Before touching any skill code, write a specification for the problem you're solving. You'll use a CSV data processing task because it's concrete and has natural edge cases.
Choose one of these:
Or define your own data processing task.
Write this to a file (skill-spec.md) or document:
# CSV Analysis Skill Specification
## Intent
[What does this skill do? Be specific about the business problem it solves]
## Input
- data_file: [type and format, e.g., "CSV with columns: customer_id, purchase_date, amount"]
- parameters: [what configuration does skill accept?]
## Output
- format: [JSON, CSV, report?]
- required_fields: [exact fields that must be in output]
- validation_rules: [how to verify output is correct]
## Success Criteria
- All data processed without loss
- Output format exactly matches specification
- Edge cases handled gracefully (malformed rows, missing values, etc.)
test cases passed, vs 10 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.